In this research, the returns of four cryptocurrencies (Bitcoin, Litecoin, Ripple and Ethereum) were analyzed in order to answer the following research question: “How do the returns of Bitcoin and other altcoins behave over time, and what can we say about extreme values for losses and profits?” With respect to volatility, cryptocurrencies can still be considered extremely volatile. For Bitcoin, the least volatile of the four, we found an annual volatility of approximately 70% based on daily exchange rates. For Ethereum, the most volatile of all four, this percentage was closer to 130%. Also, several distributions were fitted on the returns. It is shown that the Generalized Hyperbolic Distribution is the best fit for all four cryptocurrencies, apart from the tails in some cases. The tails were investigated seperately by using Extreme Value Analysis and by looking into both empirical and theoretical risk quantities (the Value at Risk and Expected Shortfall). Bitcoin appears to be the least risky of all four cryptocurrencies, but also the least profitable, whereas Ripple appears to be the most risky and also the most profitable. Compared to previous research, Bitcoin has also become less risky, showing a less fat tail for the losses than before. For Litecoin and Ripple, the reverse is true, as they appear to have become riskier. For Ethereum, no comparisons could be made, as this is a relatively new cryptocurrency that has not been investigated much yet. When tested for Paretianity, the left tails of Litecoin and Ripple appear to Pareto distributed: the losses seem to exhibit heavy tail behavior. For the profits, the tails turned out to be even heavier and can therefore also be considered Paretian. These results were confirmed by Maximum to Sum ratio plots, indicating infinite third and fourth moments for the losses and profits of Litecoin and Ripple, but not for Bitcoin and Ethereum. The results have implications for investment and risk management purposes.

In this research, Structural Equation Modelling was applied to analyze the relationships between latent variables measured by a Motivation Survey. Special attention was paid to the ordinal nature of the data. The model was split into several parts, containing Teacher Cues Monitoring and Scaffolding, Satisfaction and Frustration of the three needs Autonomy, Competence and Relatedness, and Autonomous and Controlled Motivation. The aim of this research was to find a suitable model and to discover how all of the latent variables mentioned above influence academic performance. It turns out that Academic Performance was influenced most by Autonomous Motivation, which in turn was influenced most by Autonomy Satisfaction. When leaving out Autonomy Satisfaction, Relatedness and Competence Satisfaction also appear to have a positive influence on Autonomous Motivation, which in turn are positively influenced by Monitoring and especially Scaffolding. The results have implications for education purposes, in the sense that it gives some insight with respect to teachers’ involvement in students’ academic performance.